COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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High accuracy in original datasets
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Error in compiler translation
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Low accuracy in unseen datasets
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%ERR0R#
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Detailed explanation-1: -The common pattern for overfitting can be seen on learning curve plots, where model performance on the training dataset continues to improve (e.g. loss or error continues to fall or accuracy continues to rise) and performance on the test or validation set improves to a point and then begins to get worse.
Detailed explanation-2: -Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data. When this happens, the algorithm unfortunately cannot perform accurately against unseen data, defeating its purpose.
Detailed explanation-3: -Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data.
Detailed explanation-4: -Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function.